added model

app.py

@@ -1,4 +1,52 @@
 import streamlit as st
+import torch
+from transformers import DistilBertForSequenceClassification, DistilBertTokenizer
+from torch.nn.functional import softmax
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Load the model and tokenizer
+model = DistilBertForSequenceClassification.from_pretrained('./fine_tuned_distilbert')
+tokenizer = DistilBertTokenizer.from_pretrained('./fine_tuned_distilbert')
+
+# Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Reverse mapping of categories to class labels
+reverse_mapping = {0: "BT1", 1: "BT2", 2: "BT3", 3: "BT4", 4: "BT5", 5: "BT6"}
+
+def predict_with_loaded_model(text):
+    # Tokenize the input text
+    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
+    input_ids = inputs['input_ids'].to(device)
+
+    model.eval()
+    with torch.no_grad():
+        # Get the raw logits from the model
+        outputs = model(input_ids)
+        logits = outputs.logits
+        
+        # Apply softmax to get probabilities
+        probabilities = softmax(logits, dim=-1)
+    
+    # Convert probabilities to a list or dictionary of class probabilities
+    probabilities = probabilities.squeeze().cpu().numpy()
+    
+    # Map the probabilities to the class labels using the reverse mapping
+    class_probabilities = {reverse_mapping[i]: prob for i, prob in enumerate(probabilities)}
+    
+    return class_probabilities
+
+# Streamlit App
+st.title("Question Bloom Score Prediction")
+
+# Create an input box for the user to enter a question
+question = st.text_area("Enter a question:")
+
+# If a question is entered, make the prediction
+if question:
+    class_probabilities = predict_with_loaded_model(question)
+
+    # Display the probabilities for each class label
+    st.write("**Class Probabilities (Bloom Scores)**")
+    for class_label, prob in class_probabilities.items():
+        st.write(f"{class_label}: {prob:.4f}")